Year: 2020
Type: conference-paper
Abstract: Pre-trained neural abstractive summarization systems have dominated extractive strategies on news summarization performance, at least in terms of ROUGE.However, systemgenerated abstractive summaries often face the pitfall of factual inconsistency: generating incorrect facts with respect to the source text.To address this challenge, we propose Span-Fact, a suite of two factual correction models that leverages knowledge learned from question answering models to make corrections in system-generated summaries via span selection.Our models employ single or multimasking strategies to either iteratively or autoregressively replace entities in order to ensure semantic consistency w.r.t. the source text, while retaining the syntactic structure of summaries generated by abstractive summarization models.Experiments show that our models significantly boost the factual consistency of system-generated summaries without sacrificing summary quality in terms of both automatic metrics and human evaluation.* *Most of this work was done when the first author was an intern at Microsoft.CNNDM Source (CNN) About a quarter of a million Australian homes and businesses have no power after a "once in a decade" storm battered Sydney and nearby areas.About 4,500 people
Institutions: McGill University, Microsoft (United States)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3102489149